Automated layer-wise solution for ensemble deep randomized feed-forward neural network
<p dir="ltr">The randomized feed-forward neural network is a single hidden layer feed-forward neural network that enables efficient learning by optimizing only the output weights. The ensemble deep learning framework significantly improves the performance of randomized neural network...
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| مؤلفون آخرون: | , , |
| منشور في: |
2022
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| _version_ | 1864513539494903808 |
|---|---|
| author | Minghui Hu (2457952) |
| author2 | Ruobin Gao (16003195) Ponnuthurai N. Suganthan (17347024) M. Tanveer (1758181) |
| author2_role | author author author |
| author_facet | Minghui Hu (2457952) Ruobin Gao (16003195) Ponnuthurai N. Suganthan (17347024) M. Tanveer (1758181) |
| author_role | author |
| dc.creator.none.fl_str_mv | Minghui Hu (2457952) Ruobin Gao (16003195) Ponnuthurai N. Suganthan (17347024) M. Tanveer (1758181) |
| dc.date.none.fl_str_mv | 2022-12-01T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.neucom.2022.09.148 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Automated_layer-wise_solution_for_ensemble_deep_randomized_feed-forward_neural_network/24516556 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning Randomized feed-forward neural network Random vector functional link Automated machine learning Bayesian optimization Ensemble deep random vector functional link |
| dc.title.none.fl_str_mv | Automated layer-wise solution for ensemble deep randomized feed-forward neural network |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The randomized feed-forward neural network is a single hidden layer feed-forward neural network that enables efficient learning by optimizing only the output weights. The ensemble deep learning framework significantly improves the performance of randomized neural networks. However, the framework’s capabilities are limited by traditional hyper-parameter selection approaches. Meanwhile, different random network architectures, such as the existence or lack of a direct link and the mapping of direct links, can also strongly affect the results. We present an automated learning pipeline for the ensemble deep randomized feed-forward neural network in this paper, which integrates hyper-parameter selection and randomized network architectural search via Bayesian optimization to ensure robust performance. Experiments on 46 UCI tabular datasets show that our strategy produces state-of-the-art performance on various tabular datasets among a range of randomized networks and feed-forward neural networks. We also conduct ablation studies to investigate the impact of various hyper-parameters and network architectures.</p><h2>Other Information</h2><p dir="ltr">Published in: Neurocomputing<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.neucom.2022.09.148" target="_blank">https://dx.doi.org/10.1016/j.neucom.2022.09.148</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e5351e55cd4c99e6aec1a33b95b5430c |
| identifier_str_mv | 10.1016/j.neucom.2022.09.148 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24516556 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Automated layer-wise solution for ensemble deep randomized feed-forward neural networkMinghui Hu (2457952)Ruobin Gao (16003195)Ponnuthurai N. Suganthan (17347024)M. Tanveer (1758181)Information and computing sciencesArtificial intelligenceMachine learningRandomized feed-forward neural networkRandom vector functional linkAutomated machine learningBayesian optimizationEnsemble deep random vector functional link<p dir="ltr">The randomized feed-forward neural network is a single hidden layer feed-forward neural network that enables efficient learning by optimizing only the output weights. The ensemble deep learning framework significantly improves the performance of randomized neural networks. However, the framework’s capabilities are limited by traditional hyper-parameter selection approaches. Meanwhile, different random network architectures, such as the existence or lack of a direct link and the mapping of direct links, can also strongly affect the results. We present an automated learning pipeline for the ensemble deep randomized feed-forward neural network in this paper, which integrates hyper-parameter selection and randomized network architectural search via Bayesian optimization to ensure robust performance. Experiments on 46 UCI tabular datasets show that our strategy produces state-of-the-art performance on various tabular datasets among a range of randomized networks and feed-forward neural networks. We also conduct ablation studies to investigate the impact of various hyper-parameters and network architectures.</p><h2>Other Information</h2><p dir="ltr">Published in: Neurocomputing<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.neucom.2022.09.148" target="_blank">https://dx.doi.org/10.1016/j.neucom.2022.09.148</a></p>2022-12-01T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.neucom.2022.09.148https://figshare.com/articles/journal_contribution/Automated_layer-wise_solution_for_ensemble_deep_randomized_feed-forward_neural_network/24516556CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/245165562022-12-01T15:00:00Z |
| spellingShingle | Automated layer-wise solution for ensemble deep randomized feed-forward neural network Minghui Hu (2457952) Information and computing sciences Artificial intelligence Machine learning Randomized feed-forward neural network Random vector functional link Automated machine learning Bayesian optimization Ensemble deep random vector functional link |
| status_str | publishedVersion |
| title | Automated layer-wise solution for ensemble deep randomized feed-forward neural network |
| title_full | Automated layer-wise solution for ensemble deep randomized feed-forward neural network |
| title_fullStr | Automated layer-wise solution for ensemble deep randomized feed-forward neural network |
| title_full_unstemmed | Automated layer-wise solution for ensemble deep randomized feed-forward neural network |
| title_short | Automated layer-wise solution for ensemble deep randomized feed-forward neural network |
| title_sort | Automated layer-wise solution for ensemble deep randomized feed-forward neural network |
| topic | Information and computing sciences Artificial intelligence Machine learning Randomized feed-forward neural network Random vector functional link Automated machine learning Bayesian optimization Ensemble deep random vector functional link |